Prediction of Suicidality and Violence in Hospitalized Adolescents: Comparisons by Sex
Bibliographic record
Abstract
OBJECTIVE: To examine psychological correlates of suicidality and violent behaviour in hospitalized adolescents and the extent to which these associations may be affected by their sex. METHOD: A sample of 487 psychiatric inpatients (207 male, 280 female), aged 12 to 19 years, completed a battery of psychometrically sound self-report measures of psychological functioning, substance abuse, suicidality, and violent behaviour. We conducted multiple regression analyses to determine the joint and independent predictors of suicide risk and violence risk. In subsequent analyses, we examined these associations separately by sex. RESULTS: Multiple regression analysis revealed that 9 variables (sex, age, hopelessness, self-esteem, depression, impulsivity, alcohol abuse, drug abuse, and violence risk) jointly predicted suicide risk and that an analogous model predicted violence risk. However, we found several differences with respect to which variables made significant independent contributions to these 2 predictive models. Female sex, low self-esteem, depression, drug abuse, and violence risk made independent contributions to suicide risk. Male sex, younger age, hopelessness, impulsivity, drug abuse, and suicide risk made independent contributions to violence risk. We observed a few additional differences when we considered male and female subjects separately. CONCLUSIONS: We found overlapping but distinctive patterns of prediction for suicide risk and violence risk, as well as some differences between male and female subjects. These results may reflect distinct psychological and behavioural pathways for suicidality and violence in adolescent psychiatric patients and differing risk factors for each sex. Such differences have potential implications for prevention and treatment programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".